



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			{
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   int  	=    "sew-d"
       def __init__(     self,     SCREAMING_SNAKE_CASE_=32,     SCREAMING_SNAKE_CASE_=768,     SCREAMING_SNAKE_CASE_=12,     SCREAMING_SNAKE_CASE_=12,     SCREAMING_SNAKE_CASE_=3072,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=512,     SCREAMING_SNAKE_CASE_=256,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=("p2c", "c2p"),     SCREAMING_SNAKE_CASE_="layer_norm",     SCREAMING_SNAKE_CASE_="gelu_python",     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.0,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.02,     SCREAMING_SNAKE_CASE_=1e-7,     SCREAMING_SNAKE_CASE_=1e-5,     SCREAMING_SNAKE_CASE_="group",     SCREAMING_SNAKE_CASE_="gelu",     SCREAMING_SNAKE_CASE_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512),     SCREAMING_SNAKE_CASE_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1),     SCREAMING_SNAKE_CASE_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1),     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=128,     SCREAMING_SNAKE_CASE_=16,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=0.05,     SCREAMING_SNAKE_CASE_=10,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=0.0,     SCREAMING_SNAKE_CASE_=10,     SCREAMING_SNAKE_CASE_=0,     SCREAMING_SNAKE_CASE_="mean",     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=256,     SCREAMING_SNAKE_CASE_=0,     SCREAMING_SNAKE_CASE_=1,     SCREAMING_SNAKE_CASE_=2,     **SCREAMING_SNAKE_CASE_,     )	->						Tuple:
          super().__init__(**SCREAMING_SNAKE_CASE_,     pad_token_id=SCREAMING_SNAKE_CASE_,     bos_token_id=SCREAMING_SNAKE_CASE_,     eos_token_id=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    str		      =      hidden_size
          UpperCamelCase       :    int		      =      feat_extract_norm
          UpperCamelCase       :    Any		      =      feat_extract_activation
          UpperCamelCase       :    List[str]		      =      list(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Dict		      =      list(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      list(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[Any]		      =      conv_bias
          UpperCamelCase       :    List[Any]		      =      num_conv_pos_embeddings
          UpperCamelCase       :    Tuple		      =      num_conv_pos_embedding_groups
          UpperCamelCase       :    Dict		      =      len(self.conv_dim			)
          UpperCamelCase       :    List[str]		      =      num_hidden_layers
          UpperCamelCase       :    List[str]		      =      intermediate_size
          UpperCamelCase       :    List[Any]		      =      squeeze_factor
          UpperCamelCase       :    List[str]		      =      max_position_embeddings
          UpperCamelCase       :    Optional[Any]		      =      position_buckets
          UpperCamelCase       :    Optional[int]		      =      share_att_key
          UpperCamelCase       :    List[str]		      =      relative_attention
          UpperCamelCase       :    List[str]		      =      norm_rel_ebd
          UpperCamelCase       :    List[Any]		      =      list(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      hidden_act
          UpperCamelCase       :    int		      =      num_attention_heads
          UpperCamelCase       :    Tuple		      =      hidden_dropout
          UpperCamelCase       :    Optional[int]		      =      attention_dropout
          UpperCamelCase       :    Dict		      =      activation_dropout
          UpperCamelCase       :    Union[str, Any]		      =      feat_proj_dropout
          UpperCamelCase       :    Tuple		      =      final_dropout
          UpperCamelCase       :    Union[str, Any]		      =      layer_norm_eps
          UpperCamelCase       :    Tuple		      =      feature_layer_norm_eps
          UpperCamelCase       :    Dict		      =      initializer_range
          UpperCamelCase       :    Optional[Any]		      =      vocab_size

          if (
              (len(self.conv_stride			) != self.num_feat_extract_layers)
              or (len(self.conv_kernel			) != self.num_feat_extract_layers)
              or (len(self.conv_dim			) != self.num_feat_extract_layers)
          ):
             raise ValueError(
                 'Configuration for convolutional layers is incorrect.'
                 'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
                 F"""but is `len(config.conv_dim) = {len(self.conv_dim			)}`, `len(config.conv_stride)"""
                 F"""= {len(self.conv_stride			)}`, `len(config.conv_kernel) = {len(self.conv_kernel			)}`."""			)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          UpperCamelCase       :    List[Any]		      =      apply_spec_augment
          UpperCamelCase       :    str		      =      mask_time_prob
          UpperCamelCase       :    Union[str, Any]		      =      mask_time_length
          UpperCamelCase       :    Tuple		      =      mask_time_min_masks
          UpperCamelCase       :    List[Any]		      =      mask_feature_prob
          UpperCamelCase       :    List[Any]		      =      mask_feature_length
          UpperCamelCase       :    Dict		      =      mask_feature_min_masks

          # ctc loss
          UpperCamelCase       :    int		      =      ctc_loss_reduction
          UpperCamelCase       :    Tuple		      =      ctc_zero_infinity

          # sequence classification
          UpperCamelCase       :    str		      =      use_weighted_layer_sum
          UpperCamelCase       :    Optional[Any]		      =      classifier_proj_size






       @property
       def    snake_case_			(     self			)	->						List[str]:
          return functools.reduce(operator.mul,     self.conv_stride,     1			)









'''simple docstring'''







def 						_A      ( ):






   '''simple docstring'''







   A__       =     [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

   A__       =     6
   A__       =     1
   A__       =     1901

   A__       =     0

   while year < 2001:
      day += 7

      if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
         if day > days_per_month[month - 1] and month != 2:
            month += 1
            A__       =     day - days_per_month[month - 2]
         elif day > 29 and month == 2:
            month += 1
            A__       =     day - 29
      else:
         if day > days_per_month[month - 1]:
            month += 1
            A__       =     day - days_per_month[month - 2]

      if month > 12:
         year += 1
         A__       =     1

      if year < 2001 and day == 1:
         sundays += 1
   return sundays


if __name__ == "__main__":
    print(solution())


